gradio img classify
import gradio as gr
import tensorflow as tf
import numpy as np
import requests
inception_net = tf.keras.applications.InceptionV3() # load the model
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def classify_image(inp):
inp = inp.reshape((-1, 299, 299, 3))
inp = tf.keras.applications.inception_v3.preprocess_input(inp)
prediction = inception_net.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
image = gr.inputs.Image(shape=(299, 299))
label = gr.outputs.Label(num_top_classes=3)
gr.Interface(fn=classify_image, inputs=image, outputs=label, capture_session=True).launch()
Are there any code examples left?
New code examples in category Other
-
Other 2023-03-27 22:50:10 how to select the whole line in vscode with keyboard shortcut
-
Other 2022-03-27 22:45:24 income of a web developer
-
Other 2022-03-27 22:35:01 \pyrcc_main.py: File does not exist 'resources.qrc'
-
Other 2022-03-27 22:30:45 rick roll embed code
-
Other 2022-03-27 22:20:08 Circuit_04_Potentiometer
-
Other 2022-03-27 22:20:05 iterative power
-
Other 2022-03-27 22:15:11 flutter run all
-
Other 2022-03-27 22:10:05 when is karlson release
-
Other 2022-03-27 22:10:02 wp .htaccess example
-
Other 2022-03-27 22:00:08 bash pause in file read line by line